Example usage for weka.core Instances add

Introduction

In this page you can find the example usage for weka.core Instances add.

Prototype

@Override
public boolean add(Instance instance) 

Source Link

Document

Adds one instance to the end of the set.

Usage

From source file:adams.gui.visualization.debug.objectrenderer.WekaInstancesRenderer.java

License:Open Source License

/**
 * Performs the actual rendering.//from   w w w. j av  a 2 s.  c o  m
 *
 * @param obj      the object to render
 * @param panel   the panel to render into
 * @return      null if successful, otherwise error message
 */
@Override
protected String doRender(Object obj, JPanel panel) {
    Instance inst;
    Instances data;
    InstancesTable table;
    InstancesTableModel model;
    BaseScrollPane scrollPane;
    PlainTextRenderer plain;
    SpreadSheet sheet;
    Row row;
    int i;
    SpreadSheetRenderer sprenderer;

    if (obj instanceof Instances) {
        data = (Instances) obj;
        if (data.numInstances() == 0) {
            sheet = new DefaultSpreadSheet();
            row = sheet.getHeaderRow();
            row.addCell("I").setContentAsString("Index");
            row.addCell("N").setContentAsString("Name");
            row.addCell("T").setContentAsString("Type");
            row.addCell("C").setContentAsString("Class");
            for (i = 0; i < data.numAttributes(); i++) {
                row = sheet.addRow();
                row.addCell("I").setContent(i + 1);
                row.addCell("N").setContentAsString(data.attribute(i).name());
                row.addCell("T").setContentAsString(Attribute.typeToString(data.attribute(i)));
                row.addCell("C").setContent((i == data.classIndex()) ? "true" : "");
            }
            sprenderer = new SpreadSheetRenderer();
            sprenderer.render(sheet, panel);
        } else {
            model = new InstancesTableModel(data);
            model.setShowAttributeIndex(true);
            table = new InstancesTable(model);
            scrollPane = new BaseScrollPane(table);
            panel.add(scrollPane, BorderLayout.CENTER);
        }
    } else {
        inst = (Instance) obj;
        if (inst.dataset() != null) {
            data = new Instances(inst.dataset(), 0);
            data.add((Instance) inst.copy());
            table = new InstancesTable(data);
            scrollPane = new BaseScrollPane(table);
            panel.add(scrollPane, BorderLayout.CENTER);
        } else {
            plain = new PlainTextRenderer();
            plain.render(obj, panel);
        }
    }

    return null;
}

---

From source file:adams.gui.visualization.instance.InstancePanel.java

License:Open Source License

/**
 * Returns the currently visible instances.
 *
 * @return      the instances, null if none visible
 */// w w  w.j ava2 s.c  o m
public Instances getInstances() {
    Instances result;
    Instance inst;
    int i;
    List<InstanceContainer> list;

    result = null;

    list = getContainerManager().getAllVisible();
    for (i = 0; i < list.size(); i++) {
        inst = list.get(i).getData();
        if (result == null)
            result = inst.getDatasetHeader();
        result.add(inst.toInstance());
    }

    return result;
}

---

From source file:adams.gui.visualization.instance.plotpopup.SaveVisible.java

License:Open Source License

/**
 * Returns a popup menu for the table of the container list.
 *
 * @param panel   the affected panel//  w ww. j a  v  a2 s  .com
 * @param e      the mouse event
 * @param menu   the popup menu to customize
 */
@Override
public void customize(
        DataContainerPanelWithContainerList<Instance, InstanceContainerManager, InstanceContainer> panel,
        MouseEvent e, JPopupMenu menu) {
    JMenuItem item;

    item = new JMenuItem("Save visible...", GUIHelper.getIcon("save.gif"));
    item.addActionListener((ActionEvent ae) -> {
        WekaFileChooser fc = new WekaFileChooser();
        int retval = fc.showSaveDialog(panel);
        if (retval != WekaFileChooser.APPROVE_OPTION)
            return;
        weka.core.Instances dataset = null;
        for (InstanceContainer c : panel.getTableModelContainers(true)) {
            if (dataset == null)
                dataset = new weka.core.Instances(c.getData().getDatasetHeader(), 0);
            dataset.add((weka.core.Instance) c.getData().toInstance().copy());
        }
        if (dataset == null)
            return;
        AbstractFileSaver saver = fc.getWriter();
        saver.setInstances(dataset);
        try {
            saver.setFile(fc.getSelectedFile().getAbsoluteFile());
            saver.writeBatch();
        } catch (Exception ex) {
            ex.printStackTrace();
            GUIHelper.showErrorMessage(panel, "Error saving instances:\n" + ex);
        }
    });
    menu.add(item);
}

---

From source file:adams.gui.visualization.instances.InstancesTable.java

License:Open Source License

/**
 * Exports the data.//from  w ww.  ja va  2 s  . c  o  m
 *
 * @param range   what data to export
 */
protected void saveAs(TableRowRange range) {
    int retVal;
    AbstractFileSaver saver;
    File file;
    Instances original;
    Instances data;
    int[] selRows;
    int i;

    retVal = m_FileChooser.showSaveDialog(InstancesTable.this);
    if (retVal != WekaFileChooser.APPROVE_OPTION)
        return;

    saver = m_FileChooser.getWriter();
    file = m_FileChooser.getSelectedFile();
    original = getInstances();
    switch (range) {
    case ALL:
        data = original;
        break;

    case SELECTED:
        data = new Instances(original, 0);
        selRows = getSelectedRows();
        for (i = 0; i < selRows.length; i++)
            data.add((Instance) original.instance(getActualRow(selRows[i])).copy());
        break;

    case VISIBLE:
        data = new Instances(original, 0);
        for (i = 0; i < getRowCount(); i++)
            data.add((Instance) original.instance(getActualRow(i)).copy());
        break;

    default:
        throw new IllegalStateException("Unhandled range type: " + range);
    }

    try {
        saver.setFile(file);
        saver.setInstances(data);
        saver.writeBatch();
    } catch (Exception ex) {
        GUIHelper.showErrorMessage(InstancesTable.this, "Failed to save data (" + range + ") to: " + file, ex);
    }
}

---

From source file:adams.gui.visualization.instances.InstancesTableModel.java

License:Open Source License

/**
 * sorts the instances via the given attribute
 *
 * @param columnIndex the index of the column
 * @param ascending ascending if true, otherwise descending
 *///from  w w  w.jav  a 2 s .c om
public void sortInstances(int columnIndex, boolean ascending) {
    int offset;

    offset = 1;
    if (m_ShowWeightsColumn)
        offset++;

    if ((columnIndex >= offset) && (columnIndex < getColumnCount())) {
        addUndoPoint();
        m_Data.stableSort(columnIndex - offset);
        if (!ascending) {
            Instances reversedData = new Instances(m_Data, m_Data.numInstances());
            int i = m_Data.numInstances();
            while (i > 0) {
                i--;
                int equalCount = 1;
                while ((i > 0) && (m_Data.instance(i).value(columnIndex - offset) == m_Data.instance(i - 1)
                        .value(columnIndex - offset))) {
                    equalCount++;
                    i--;
                }
                int j = 0;
                while (j < equalCount) {
                    reversedData.add(m_Data.instance(i + j));
                    j++;
                }
            }
            m_Data = reversedData;
        }
        notifyListener(new TableModelEvent(this));
    }
}

---

From source file:ann.ANN.java

public void percentageSplit(Classifier model, double percent, Instances data) {
    try {/* ww w.j a  v  a  2s . com*/
        int trainSize = (int) Math.round(data.numInstances() * percent / 100);
        int testSize = data.numInstances() - trainSize;
        Instances train = new Instances(data, trainSize);
        Instances test = new Instances(data, testSize);
        ;

        for (int i = 0; i < trainSize; i++) {
            train.add(data.instance(i));
        }
        for (int i = trainSize; i < data.numInstances(); i++) {
            test.add(data.instance(i));
        }

        Evaluation eval = new Evaluation(train);
        eval.evaluateModel(model, test);
        System.out.println("================================");
        System.out.println("========Percentage  Split=======");
        System.out.println("================================");
        System.out.println(eval.toSummaryString("\n=== Summary ===\n", false));
        System.out.println(eval.toClassDetailsString("=== Detailed Accuracy By Class ===\n"));
        System.out.println(eval.toMatrixString("=== Confusion Matrix ===\n"));
    } catch (Exception ex) {
        System.out.println("File tidak berhasil di-load");
    }
}

---

From source file:aprendizadodemaquina.Transformador.java

License:Open Source License

public void paraDt(File arquivoSaida) {

    // Gera um objeto Instances ("cabealho" para instancias do Weka)
    Instances dadosTreinamento = featurizador.geraInstances();

    Set<Par> paresTermosUsados = new HashSet<Par>();

    // Gera as instancias de treinamento para cada sentena
    for (Sentenca s : listaSentencas) {

        s.removerTermosNaoUtilizados();/*from  w  w w  .  j  av  a 2 s.co m*/

        // Instancias positivas (relaoes marcadas)
        for (Relacao r : s.getRelacoes()) {
            Par p = new Par(s, r.getTermo1(), r.getTermo2());
            if (paresTermosUsados.contains(p))
                continue;
            dadosTreinamento.add(featurizador.paraInstancia(s, r, r.getTermo1(), r.getTermo2(), "treinamento"));
            paresTermosUsados.add(p);
        }
        // Instancias negativas (todas os pares de termos nao marcados como relaoes)
        for (Termo t1 : s.getTermos()) {
            for (Termo t2 : s.getTermos()) {
                if (t1.equals(t2) || paresTermosUsados.contains(new Par(s, t1, t2)))
                    continue;
                dadosTreinamento.add(featurizador.paraInstancia(s, null, t1, t2, "negativa"));
            }
        }
    }

    // Salva o conjunto de dados no arquivo de saida
    try {
        ArffSaver arffSaver = new ArffSaver();
        arffSaver.setInstances(dadosTreinamento);
        arffSaver.setFile(arquivoSaida);
        arffSaver.writeBatch();
        Logger.getLogger("ARS logger").log(Level.INFO, "Conjunto de dados de treinamento salvo no arquivo {0}",
                arquivoSaida.getAbsolutePath());
    } catch (IOException ex) {
        Logger.getLogger("ARS logger").log(Level.SEVERE, null, ex);
    }

}

---

From source file:arffcreator.arffFrame.java

private void createActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_createActionPerformed
    // TODO add your handling code here:

    FastVector atts;/*from  www  .  ja v a  2s. c o m*/
    FastVector attsRel;
    FastVector attVals;
    FastVector attValsRel;
    Instances data;
    Instances dataRel;
    double[] vals;
    double[] valsRel;
    int i;

    // 1. set up attributes
    atts = new FastVector();
    // - numeric
    atts.addElement(new Attribute("att1"));
    // - nominal
    attVals = new FastVector();
    for (i = 0; i < 5; i++)
        attVals.addElement("val" + (i + 1));
    atts.addElement(new Attribute("att2", attVals));
    // - string
    atts.addElement(new Attribute("att3", (FastVector) null));
    // - date
    atts.addElement(new Attribute("att4", "yyyy-MM-dd"));
    // - relational
    attsRel = new FastVector();
    // -- numeric
    attsRel.addElement(new Attribute("att5.1"));
    // -- nominal
    attValsRel = new FastVector();
    for (i = 0; i < 5; i++)
        attValsRel.addElement("val5." + (i + 1));
    attsRel.addElement(new Attribute("att5.2", attValsRel));
    dataRel = new Instances("att5", attsRel, 0);
    atts.addElement(new Attribute("att5", dataRel, 0));

    // 2. create Instances object
    data = new Instances("MyRelation", atts, 0);

    // 3. fill with data
    // first instance
    vals = new double[data.numAttributes()];
    // - numeric
    vals[0] = Math.PI;
    // - nominal
    vals[1] = attVals.indexOf("val3");
    // - string
    vals[2] = data.attribute(2).addStringValue("This is a string!");
    try {
        // - date
        vals[3] = data.attribute(3).parseDate("2015-07-30");
    } catch (ParseException ex) {
        Logger.getLogger(arffFrame.class.getName()).log(Level.SEVERE, null, ex);
    }
    // - relational
    dataRel = new Instances(data.attribute(4).relation(), 0);
    // -- first instance
    valsRel = new double[2];
    valsRel[0] = Math.PI + 1;
    valsRel[1] = attValsRel.indexOf("val5.3");
    dataRel.add(new Instance(1.0, valsRel));
    // -- second instance
    valsRel = new double[2];
    valsRel[0] = Math.PI + 2;
    valsRel[1] = attValsRel.indexOf("val5.2");
    dataRel.add(new Instance(1.0, valsRel));
    vals[4] = data.attribute(4).addRelation(dataRel);
    // add
    data.add(new Instance(1.0, vals));

    // second instance
    vals = new double[data.numAttributes()]; // important: needs NEW array!
    // - numeric
    vals[0] = Math.E;
    // - nominal
    vals[1] = attVals.indexOf("val1");
    // - string
    vals[2] = data.attribute(2).addStringValue("And another one!");
    try {
        // - date
        vals[3] = data.attribute(3).parseDate("2015-07-30");
    } catch (ParseException ex) {
        Logger.getLogger(arffFrame.class.getName()).log(Level.SEVERE, null, ex);
    }
    // - relational
    dataRel = new Instances(data.attribute(4).relation(), 0);
    // -- first instance
    valsRel = new double[2];
    valsRel[0] = Math.E + 1;
    valsRel[1] = attValsRel.indexOf("val5.4");
    dataRel.add(new Instance(1.0, valsRel));
    // -- second instance
    valsRel = new double[2];
    valsRel[0] = Math.E + 2;
    valsRel[1] = attValsRel.indexOf("val5.1");
    dataRel.add(new Instance(1.0, valsRel));
    vals[4] = data.attribute(4).addRelation(dataRel);
    // add
    data.add(new Instance(1.0, vals));

    // 4. output data

    textArea.append(data.toString());

    dataset = data.toString();

}

---

From source file:arffGenerator.TextDirectoryToArff.java

License:Open Source License

/**
 * Crea instancias a partir del texto plano que se encuentra en los archivos dentro de las carpetas del path especificado.
 * Las instancias tendrán como atributos un nominal con la clase (seran los nombres de las carpetas que contienen los archivos) 
 * y un String con el texto que aparece en los ficheros.
 * @param clase - String que indica los posibles valores de la clase en el fichero arff que se creará.
 * @param directoryPath - String que indica el path del directorio donde se encuentran los ficheros/directorios con las instancias.
 * @param data - Conjunto de instancias que forman la estructura para guardar las nuevas instancias.
 *///from   w w w . ja  va2  s .  c  o  m
private void cargarAtrribDeClase(String clase, String directoryPath, Instances data) {
    System.out.println("Se crean las instancias de la clase: " + clase);
    File dir = new File(directoryPath);
    String[] files = dir.list();
    for (int i = 0; i < files.length; i++) {
        if (files[i].endsWith(".txt")) {
            try {
                double[] newInst = new double[2];
                File txt = new File(directoryPath + File.separator + files[i]);
                InputStreamReader is;
                is = new InputStreamReader(new FileInputStream(txt));
                StringBuffer txtStr = new StringBuffer();
                int c;
                while ((c = is.read()) != -1) {
                    c = (char) c;
                    if (!isFakeChar((char) c))
                        txtStr.append((char) c);
                }
                newInst[0] = (double) data.attribute(0).addStringValue(txtStr.toString());
                if (clase == null) {
                    newInst[1] = Double.NaN;
                } else {
                    newInst[1] = (double) data.attribute(1).indexOfValue(clase);
                }
                data.add(new Instance(1.0, newInst));
                is.close();
            } catch (Exception e) {
                System.err.println("failed to convert file: " + directoryPath + File.separator + files[i]);
            }
        }
    }
    System.out.println("Se crearon las instancias de la clase: " + clase);
}

---

From source file:at.aictopic1.sentimentanalysis.machinelearning.impl.TwitterClassifer.java

public Integer classify(Tweet[] tweets) {
    // TEST//  w  ww  .j  a v a2 s  .  c  om

    // Generate two tweet examples
    Tweet exOne = new Tweet("This is good and fantastic");
    exOne.setPreprocessedText("This is good and fantastic");
    Tweet exTwo = new Tweet("Horribly, terribly bad and more");
    exTwo.setPreprocessedText("Horribly, terribly bad and more");
    Tweet exThree = new Tweet(
            "I want to update lj and read my friends list, but I\\'m groggy and sick and blargh.");
    exThree.setPreprocessedText(
            "I want to update lj and read my friends list, but I\\'m groggy and sick and blargh.");
    Tweet exFour = new Tweet("bad hate worst sick");
    exFour.setPreprocessedText("bad hate worst sick");
    tweets = new Tweet[] { exOne, exTwo, exThree, exFour };
    // TEST

    // Load model
    //        loadModel();
    // Convert Tweet to Instance type 
    // Get String Data
    // Create attributes for the Instances set
    Attribute twitter_id = new Attribute("twitter_id");
    //        Attribute body = new Attribute("body");

    FastVector classVal = new FastVector(2);
    classVal.addElement("pos");
    classVal.addElement("neg");

    Attribute class_attr = new Attribute("class_attr", classVal);

    // Add them to a list
    FastVector attrVector = new FastVector(3);
    //        attrVector.addElement(twitter_id);
    //        attrVector.addElement(new Attribute("body", (FastVector) null));
    //        attrVector.addElement(class_attr);

    // Get the number of tweets and then create predictSet
    int numTweets = tweets.length;
    Enumeration structAttrs = dataStructure.enumerateAttributes();

    //        ArrayList<Attribute> attrList = new ArrayList<Attribute>(dataStructure.numAttributes());
    while (structAttrs.hasMoreElements()) {
        attrVector.addElement((Attribute) structAttrs.nextElement());
    }
    Instances predictSet = new Instances("predictInstances", attrVector, numTweets);
    //        Instances predictSet = new Instances(dataStructure);
    predictSet.setClassIndex(2);

    // init prediction
    double prediction = -1;

    System.out.println("PredictSet matches source structure: " + predictSet.equalHeaders(dataStructure));

    System.out.println("PredSet struct: " + predictSet.attribute(0));
    System.out.println("PredSet struct: " + predictSet.attribute(1));
    System.out.println("PredSet struct: " + predictSet.attribute(2));
    // Array to return predictions 
    //double[] tweetsClassified = new double[2][numTweets];
    //List<Integer, Double> tweetsClass = new ArrayList<Integer, Double>(numTweets);
    for (int i = 0; i < numTweets; i++) {
        String content = (String) tweets[i].getPreprocessedText();

        System.out.println("Tweet content: " + content);

        //            attrList
        Instance tweetInstance = new Instance(predictSet.numAttributes());

        tweetInstance.setDataset(predictSet);

        tweetInstance.setValue(predictSet.attribute(0), i);
        tweetInstance.setValue(predictSet.attribute(1), content);
        tweetInstance.setClassMissing();

        predictSet.add(tweetInstance);

        try {
            // Apply string filter
            StringToWordVector filter = new StringToWordVector();

            filter.setInputFormat(predictSet);
            Instances filteredPredictSet = Filter.useFilter(predictSet, filter);

            // Apply model
            prediction = trainedModel.classifyInstance(filteredPredictSet.instance(i));
            filteredPredictSet.instance(i).setClassValue(prediction);
            System.out.println("Classification: " + filteredPredictSet.instance(i).toString());
            System.out.println("Prediction: " + prediction);

        } catch (Exception ex) {
            Logger.getLogger(TwitterClassifer.class.getName()).log(Level.SEVERE, null, ex);
        }
    }

    return 0;
}

---

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